局部自适应复合绝缘子检测与憎水性分类网络

憎水性等级(Hydrophobicity Class，HC)是衡量绝缘子性能的重要指标之一。在实际环境的多种因素作用下绝缘子伞裙表面存在局部憎水性差异，为了准确识别绝缘子的性能，本文提出了一种基于深度学习的局部自适应绝缘子检测与憎水性分类模型。首先，通过绝缘子分割模块分离绝缘子与背景区域，为后续针对绝缘子区域的操作提供分割信息；然后将绝缘子区域划分为固定大小的图像块，在缩小分辨率减小运算难度的同时保留了绝缘子表面的细节信息；最后通过憎水性分类模块分析图像块内绝缘子的憎水性。实验使用巡检维护现场的绝缘子图片作为样本集，分阶段构建模型，分别对分割阶段和憎水性分类阶段的准确性进行评估。实验结果显示分割阶段模块能有效识别绝缘子和背景区域，交叉验证的测试集准确率均大于97.21%，并且憎水性分类阶段模块能准确分析绝缘子憎水性，对140幅测试图片的识别准确率达到98.65%。经过实验证明本文提出的模型在复杂自然环境中检测绝缘子性能是一种有效的解决方案。

Region adaptation network for composite insulator segmentation and hydrophobicity recognition

Hydrophobicity Class (HC) is one of important indexes to measure the performance of composite insulators. The hydrophobicity of insulator shed is different on the part surface for the various factors in the natural environment. In order to judge the performance of insulator, this paper proposes a region adaptation method for insulator segmentation and hydrophobicity recognition based on deep learning. First, separate the insulator area and the background by the insulator segment module, which provides segment information for the later operators on the insulator area; then, the insulator area is cropped into several image blocks with the fixed resolution, which can reduce the resolution and the operational complexity while preserving the insulator surface details; finally, judge the hydrophobicity class of insulator by the hydrophobicity classification module. The experiment dataset from maintenance sites is used to build model in stages and evaluate separately the accuracy of the stage of segment and HC classification. The experiment results show that the segment stage module can identify the insulator regions and the background, whose accuracy on the cross-validation test dataset is greater than 97.21%, and the HC classification stage module can classify the HC of insulators, whose accuracy of 140 test images can reach 98.65%. The proposed model is proven to be an effective solution to checking insulators performance in complex natural environment by experiments.